pass | R Documentation |

Implements the PASS (Proportion Adaptive Segment Selection) procedure of Jeng et al. (2012). PASS uses a higher criticism statistic to pool the information about the presence or absence of a collective anomaly across the components. It uses Circular Binary Segmentation to detect multiple collective anomalies.

```
pass(x, alpha = 2, lambda = NULL, max_seg_len = 10, min_seg_len = 1)
```

`x` |
A numeric matrix with n rows and p columns containing the data which is to be inspected. The time series data classes ts, xts, and zoo are also supported. |

`alpha` |
A positive integer > 0. This value is used to stabilise the higher criticism based test statistic used by PASS leading to a better finite sample familywise error rate. Anomalies affecting fewer than alpha components will however in all likelihood escape detection. The default is 2. |

`lambda` |
A positive real value setting the threshold value for the familywise Type 1 error. The default value
is |

`max_seg_len` |
A positive integer ( |

`min_seg_len` |
A positive integer ( |

An instance of an S4 object of type `.pass.class`

containing the data `X`

, procedure parameter values, and the results.

10.1093/biomet/ass059anomaly

```
library(anomaly)
# generate some multivariate data
data(simulated)
res<-pass(sim.data)
summary(res)
plot(res,variate_names=TRUE)
```

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